Method and system for maintaining knowledge required in a decision-making process framework

ABSTRACT

Disclosed is a knowledge system for retrieving a knowledge object, pertaining to a query, in a cognitive decision-making process. The knowledge system comprises a knowledge access module and a knowledge processing module. The knowledge access module may receive a knowledge request requesting a knowledge object. In one aspect, the knowledge request may be associated to at least one domain. The knowledge access further generates a structured query based on the knowledge request. The knowledge access module further splits the structured query into one or more sub queries. In one aspect, the structured query may be split based on the at least one domain and metadata associated to the at least one domain. The knowledge access module further fetches one or more knowledge objects for each sub query upon executing the one or more sub queries on a system database storing a plurality of knowledge objects. In one aspect, the one or more knowledge objects may be fetched upon referring to one or more ontologies stored in the system database. The knowledge processing module creates and thereby stores an integrated knowledge object for the knowledge request upon integrating the one or more knowledge objects fetched by the one or more sub queries.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application claims priority from U.S. ProvisionalApplication No. 62/410,791 filed on Oct. 20, 2016, the entirety of whichis hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure described herein, in general, relates to maintainknowledge required in a decision-making process framework.

BACKGROUND

It has been proven that providing relevant knowledge at the right timemay play a significant role in optimizing the performance of a business.In an era of Information Technology (IT), vast amount of knowledge maybe electronically accessible from knowledge management systems via acomputer network. However, such knowledge management systems have proveninadequate in providing the relevant knowledge. This is due to the factthat traditional knowledge management systems mostly rely on a queryderived by a human. As a result of which the query may result intoirrelevant knowledge as such query is derived without consideringcontextual and conceptual parameters associated to the business.

SUMMARY

Before the present systems and methods, are described, it is to beunderstood that this application is not limited to the particularsystems, and methodologies described, as there can be multiple possibleembodiments which are not expressly illustrated in the presentdisclosure. It is also to be understood that the terminology used in thedescription is for the purpose of describing the particular versions orembodiments only, and is not intended to limit the scope of the presentapplication. This summary is provided to introduce concepts related tosystems and methods for retrieving a knowledge object, pertaining to aquery, in a cognitive decision-making process and the concepts arefurther described below in the detailed description. This summary is notintended to identify essential features of the claimed subject matternor is it intended for use in determining or limiting the scope of theclaimed subject matter.

In one implementation, a knowledge system for retrieving a knowledgeobject, pertaining to a query, in a cognitive decision-making process isdisclosed. The knowledge system comprises a processor and a memorycoupled to the processor. The processor is capable of executing aplurality of modules stored in the memory. The knowledge systemcomprises a knowledge access module and a knowledge processing module.The knowledge access module may receive a knowledge request requesting aknowledge object. In one aspect, the knowledge request may be associatedto at least one domain. It may be noted that the knowledge request maybe received from a user. The knowledge access module may furthergenerate a structured query based on the knowledge request. Theknowledge access module may further split the structured query into oneor more sub queries. In one aspect, the structured query may be splitbased on the at least one domain and metadata associated to the at leastone domain. The knowledge access module may further fetch one or moreknowledge objects for each sub query upon executing the one or more subqueries on a system database storing a plurality of knowledge objects.In one aspect, the one or more knowledge objects may be fetched uponreferring to one or more ontologies stored in the system database. Theknowledge processing module may create an integrated knowledge objectfor the knowledge request upon integrating the one or more knowledgeobjects fetched by the one or more sub queries. The knowledge processingmodule may further store the integrated knowledge object, along with theone or more ontologies, in a temporary memory for the user's referencethereby retrieving the knowledge object pertaining to the query.

In another implementation, a method for retrieving a knowledge object,pertaining to a query, in a cognitive decision-making process isdisclosed. In order to retrieve the knowledge object, a knowledgerequest requesting a knowledge object may be received from a user. Inone aspect, the knowledge request may be associated to at least onedomain. Upon receipt of the knowledge request, a structured query may begenerated based on the knowledge request. Subsequently, the structuredquery may be split into one or more sub queries. In one aspect, thestructured query may be split based on the at least one domain andmetadata associated to the at least one domain. Post splitting of thestructured query, one or more knowledge objects may be fetched for eachsub query upon executing the one or more sub queries on a systemdatabase storing a plurality of knowledge objects. In one aspect, theone or more knowledge objects may be fetched upon referring to one ormore ontologies stored in the system database. After fetching the one ormore knowledge objects, an integrated knowledge object may be createdfor the knowledge request upon integrating the one or more knowledgeobjects fetched by the one or more sub queries. Thereafter, theintegrated knowledge object, along with the one or more ontologies, maybe stored in a temporary memory for the user's reference therebyretrieving the knowledge object pertaining to the query. In one aspect,the aforementioned method for retrieving the knowledge object may beperformed by a processor using programmed instructions stored in amemory of the knowledge system.

In yet another implementation, a non-transitory computer readable mediumembodying a program executable in a computing device for retrieving aknowledge object, pertaining to a query, in a cognitive decision-makingprocess is disclosed. The program may comprise a program code forreceiving a knowledge request requesting a knowledge object, wherein theknowledge request is associated to at least one domain, and wherein theknowledge request is received from a user. The program may furthercomprise a program code for generating, by the processor, a structuredquery based on the knowledge request. The program may further comprise aprogram code for splitting the structured query into one or more subqueries, wherein the structured query is split based on the at least onedomain and metadata associated to the at least one domain. The programmay further comprise a program code for fetching one or more knowledgeobjects for each sub query upon executing the one or more sub queries ona system database storing a plurality of knowledge objects, wherein theone or more knowledge objects are fetched upon referring to one or moreontologies stored in the system database. The program may furthercomprise a program code for creating an integrated knowledge object forthe knowledge request upon integrating the one or more knowledge objectsfetched by the one or more sub queries. The program may further comprisea program code for storing the integrated knowledge object, along withthe one or more ontologies, in a temporary memory for the user'sreference thereby retrieving the knowledge object pertaining to thequery.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing detailed description of embodiments is better understoodwhen read in conjunction with the appended drawing. For the purpose ofillustrating the disclosure, there is shown in the present documentexample constructions of the disclosure; however, the disclosure is notlimited to the specific methods and apparatus disclosed in the documentand the drawings.

The detailed description is described with reference to the accompanyingfigure. In the figure, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Thesame numbers are used throughout the drawings to refer like features andcomponents.

FIG. 1 illustrates a network implementation of a knowledge system forretrieving a knowledge object in a decision-making process framework isshown, in accordance with an embodiment of the present disclosure.

FIG. 2 illustrates the knowledge system, in accordance with anembodiment of the present disclosure.

FIG. 3 illustrates a method for retrieving the knowledge object in thedecision-making process framework is shown, in accordance with anembodiment of the present disclosure.

The figure depicts an embodiment of the present disclosure for purposesof illustration only. One skilled in the art will readily recognize fromthe following discussion that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles of the disclosure described herein.

DETAILED DESCRIPTION

Some embodiments of this disclosure, illustrating all its features, willnow be discussed in detail. The words “comprising,” “having,”“containing,” and “including,” and other forms thereof, are intended tobe equivalent in meaning and be open ended in that an item or itemsfollowing any one of these words is not meant to be an exhaustivelisting of such item or items, or meant to be limited to only the listeditem or items. It must also be noted that, the singular forms “a,” “an,”and “the” include plural references unless the context clearly dictatesotherwise. Although any apparatuses and methods similar or equivalent tothose described herein can be used in the practice or testing ofembodiments of the present disclosure, the exemplary, apparatuses andmethods are now described. The disclosed embodiments are merelyexemplary of the disclosure, which may be embodied in various forms.

Various modifications to the embodiment will be readily apparent tothose skilled in the art and the generic principles herein may beapplied to other embodiments. However, one of ordinary skill in the artwill readily recognize that the present disclosure is not intended to belimited to the embodiments illustrated, but is to be accorded the widestscope consistent with the principles and features described herein.

Optimization in business processes is necessitated for scaling up abusiness. In order to optimize a business performance, a cognitivesystem continually senses for new signals and reacts promptly when suchsignals indicate a brewing business situation of interest hereinafterreferred to as an ‘Opportunity’. This is to signify that, for eachopportunity, the business performance is optimized by mitigating riskand/or by seizing a growth prospect. The extent to and the dexterity bywhich the business may sense and react to the opportunity indicatescapability for continuous optimization of the business performance.

It may be noted that an opportunity driven organization optimizes thebusiness performance by using a cognitive decision-making process bysensing and reacting to the opportunity. The cognitive decision-makingprocess comprises four phases including ‘SENSE’, ‘EXPLORE’, ‘ACT’, and‘LEARN’. Each opportunity instance originates in the SENSE phase, moveson to EXPLORE, then onto ACT phase, and finally closes in the LEARNphase. However, to sense and thereby reacts to the opportunity, itbecomes utmost important to provide relevant knowledge, pertaining tothe opportunity, so that the opportunity driven organization may takecorrective measures for the opportunity.

The present invention facilitates to retrieve a knowledge object in thecognitive decision-making process is disclosed. In other words, thepresent invention maintains a knowledge base comprising a plurality ofknowledge objects (i.e. all declarative and procedural knowledge) andthereby retrieves a knowledge object relevant for the businessopportunity for optimizing the business performance. Examples of theknowledge object may include, but not limited to, a document, a webpage, a video, and an image. In one aspect, the knowledge object, storedin a knowledge system, may be retrieved to draw inferences and therebyprovided to the SEAL framework in order to assist a user in takingnecessary decisions based on the inferences drawn from the knowledgeobject.

In one aspect, the knowledge object may be stored, in the knowledgesystem, corresponding to one or more ontologies. It may be noted thatthe one or more ontologies store knowledge about opportunity types,opportunity instances, users, system configuration, and parameters andhyper-parameters of analytic methods. Examples of the one or moreontologies may include, but not limited to, perceptual system ontology,domain opportunity ontology, cognitive decision-making process ontology,domain knowledge ontology, and Human Interaction System ontology.

In order to retrieve the knowledge object in the cognitivedecision-making process (SEAL), the knowledge system receives a requestfrom a user. The request may be associated to at least one domain. Therequest may then be used to generate a structured query. The knowledgesystem further pre-processes (for example, check if well-formed) thestructured query, if necessary, before executing on a knowledge databasestored in the knowledge system. Upon execution of the query, theknowledge system fetches the knowledge object and further display theknowledge object to the user. The knowledge object may then be used todeduce inferences/insights obtained and taking the corrective measuresfor the business opportunity.

Thus, in this manner, the knowledge system retrieves the knowledgeobject in the cognitive decision-making process. While aspects ofdescribed system and method for retrieving the knowledge object may beimplemented in any number of different computing systems, environments,and/or configurations, the embodiments are described in the context ofthe following exemplary system.

Referring now to FIG. 1, a network implementation 100 of a knowledgesystem 102 for retrieving a knowledge object, pertaining to a query, ina cognitive decision-making process is disclosed. In order to retrievethe knowledge object, the knowledge system 102 receives a knowledgerequest requesting a knowledge object from a user. Upon receipt of theknowledge request, the knowledge system 102 generates a structured querybased on the knowledge request. Subsequently, knowledge system 102splits the structured query into one or more sub queries. In one aspect,the structured query may be split based on the at least one domain andmetadata associated to the at least one domain. Post splitting of thestructured query, the knowledge system 102 fetches one or more knowledgeobjects for each sub query upon executing the one or more sub queries ona system database storing a plurality of knowledge objects. In oneaspect, the one or more knowledge objects may be fetched upon referringto one or more ontologies stored in the system database. After fetchingthe one or more knowledge objects, the knowledge system 102 creates anintegrated knowledge object for the knowledge request upon integratingthe one or more knowledge objects fetched by the one or more subqueries. Thereafter, the knowledge system 102 stores the integratedknowledge object, along with the one or more ontologies, in a temporarymemory for the user's reference thereby retrieving the knowledge objectpertaining to the query.

Although the present disclosure is explained considering that theknowledge system 102 is implemented on a single server, it may beunderstood that the knowledge system 102 may also be implemented in aDistributed Computing Environment (DCE), involving variety of computingsystems operating in parallel. Examples of the computing systems mayinclude, but not limited to, a laptop computer, a desktop computer, anotebook, a workstation, a mainframe computer, a server, a networkserver, and the like. It will be understood that the knowledge system102 may be accessed by multiple users through one or more user devices104-1, 104-2, 104-3; 104-N. In one implementation, the knowledge system102 may comprise the cloud-based computing environment in which a usermay operate individual computing systems configured to execute remotelylocated applications. Examples of the user devices 104 may include, butare not limited to, a portable computer, a personal digital assistant, ahandheld device, and a workstation. The user devices 104 arecommunicatively coupled to the knowledge system 102 through a network106.

In one implementation, the network 106 may be a wireless network, awired network or a combination thereof. The network 106 can beimplemented as one of the different types of networks, such as intranet,local area network (LAN), wide area network (WAN), the internet, and thelike. The network 106 may either be a dedicated network or a sharednetwork. The shared network represents an association of the differenttypes of networks that use a variety of protocols, for example,Hypertext Transfer Protocol (HTTP), Transmission ControlProtocol/Internet Protocol (TCP/IP), Wireless Application Protocol(WAP), and the like, to communicate with one another. Further thenetwork 106 may include a variety of network devices, including routers,bridges, servers, computing devices, storage devices, and the like.

Referring now to FIG. 2, the knowledge system 102 is illustrated inaccordance with an embodiment of the present subject matter. In oneembodiment, the Knowledge system 102 may include at least one processor202, an input/output (I/O) interface 204, and a memory 206. The at leastone processor 202 may be implemented as one or more microprocessors,microcomputers, microcontrollers, digital signal processors, centralprocessing units, state machines, logic circuitries, and/or any devicesthat manipulate signals based on operational instructions. Among othercapabilities, the at least one processor 202 is configured to fetch andexecute computer-readable instructions stored in the memory 206.

The I/O interface 204 may include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface,and the like. The I/O interface 204 may allow the knowledge system 102to interact with the user directly or through the user devices 104.Further, the I/O interface 204 may enable the knowledge system 102 tocommunicate with other computing devices, such as web servers andexternal data servers (not shown). The I/O interface 204 can facilitatemultiple communications within a wide variety of networks and protocoltypes, including wired networks, for example, LAN, cable, etc., andwireless networks, such as WLAN, cellular, or satellite. The I/Ointerface 204 may include one or more ports for connecting a number ofdevices to one another or to another server.

The memory 206 may include any computer-readable medium or computerprogram product known in the art including, for example, volatilememory, such as static random-access memory (SRAM) and dynamic randomaccess memory (DRAM), and/or non-volatile memory, such as read onlymemory (ROM), erasable programmable ROM, flash memories, hard disks,optical disks, and magnetic tapes. The memory 206 may include modules208 and data 210.

The modules 208 include routines, programs, objects, components, datastructures, etc., which perform particular tasks or implement particularabstract data types. In one implementation, the modules 208 may includea knowledge access module 212, a knowledge processing module 214, aknowledge persistence module 216, and other modules 218. The othermodules 218 may include programs or coded instructions that supplementapplications and functions of the knowledge system 102. The modules 208described herein may be implemented as software modules that may beexecuted in the cloud-based computing environment of the knowledgesystem 102.

The data 210, amongst other things, serves as a repository for storing aplurality of knowledge objects. It may be noted that the data 210 mayinclude a system database 220 present within the knowledge system 102and an external database (not shown) communicative coupled, via thenetwork 106, with the knowledge system 102. The system database 220further comprises one or more ontologies storing a set of predefinedrules facilitating fetching of a knowledge object relevant for a query.The one or more ontologies including a cognitive decision-making processontology 222, a perceptual system ontology 224, a domain opportunityontology 226, a domain knowledge ontology 228, a human interactionsystem ontology 230, and other data 232. The other data 232 may includedata generated as a result of the execution of one or more modules inthe other modules 218. The detailed description of the modules 208 alongwith other components of the knowledge system 102 is further explainedby referring to FIG. 2.

Further referring to the FIG. 2. The knowledge system 102 retrieves aknowledge object in the cognitive decision-making process is disclosed.In other words, the present invention maintains a knowledge databasecomprising a plurality of knowledge objects (i.e. all declarative andprocedural knowledge) and thereby retrieves a knowledge object relevantfor the business opportunity for optimizing the business performance.Examples of the knowledge object may include, but not limited to, adocument, a web page, a video, and an image. In one aspect, theknowledge object, stored in a knowledge system 102, may be retrieved todraw inferences and thereby provided to the SEAL framework in order toassist a user in taking necessary decisions based on the inferencesdrawn from the knowledge object. To retrieve the knowledge object, theknowledge system 102 may employ the knowledge access module 212, theknowledge processing module 214, and the knowledge persistence module216.

At first, the knowledge access module 212 receives a knowledge request,requesting a knowledge object, from the user. In one aspect, theknowledge request may be associated to at least one domain. It may beunderstood that the knowledge request may be provided by the user in anarrative form, wherein the knowledge request requests information aboutthe opportunity so as to take necessary decisions. Upon receipt of theknowledge request, the knowledge access module 212 generates astructured query based on the knowledge request. In one embodiment, thestructured query is generated by using one or more machine learningmethod including a deep learning network pre-trained for the knowledgerequest and a structured query language type. Examples of the structuredquery may include, but not limited to, Structured Query Language (SQL)and SPARQL. In one embodiment, upon generation, the structured query maybe pre-processed (for example, check if well-formed in accordance withthe rules pertaining to the SQL or SPARQL) before executing on thestructured query on the system database 220.

Upon generating the structured query, the knowledge access module 212splits the structured query into one or more sub queries. In one aspect,the structured query may be split based on the at least one domain andmetadata associated to the at least one domain. In one aspect, themetadata indicates one or more sub sections associated to the at leastone domain. In one embodiment, each sub query may be assigned with anactor responsible for fetching at least one knowledge object, from thesystem database 220, relevant for a sub query. In one aspect, the actoris a mathematical model for performing computations in order to retrieveresults pertaining to the sub query.

Post splitting of the structured query, the knowledge access module 212enables the actor to fetch one or more knowledge objects for each subquery upon executing the one or more sub queries. It may be understoodthat each sub query may retrieve the knowledge objects from multipledatabases. Therefore, the one or more sub queries may be executed, inparallel or in a distributed manner, over the multiple databases presentin the Distributed Computing Environment (DCE). It may be noted that theexecution of the one or more sub queries over the multiple database maydecrease the processing time spent for retrieving the knowledge objects.

In one embodiment, the knowledge access module 212 fetches the one ormore knowledge objects upon referring to the one or more ontologiesstored in the system database 220. Examples of the one or moreontologies comprises the cognitive decision-making process ontology 222,the perceptual system ontology 224, the domain opportunity ontology 226,the domain knowledge ontology 228, and the human interaction systemontology 230. The one or more ontologies may comprise a set ofpredefined rules facilitating fetching of a knowledge object relevantfor a sub query. In one embodiment, the set of predefined rulescomprises relationship among domains and a set of scenarios indicating achange in the relationship among the domains over a period of time

In other words, the set of predefined rules comprises describingrelationships among domains and meta-rules about basic rules and so on.For example, the rules may be about a merchandize hierarchy and itsevolution. It may be noted that SKU's are organized in a merchandizehierarchy by introducing categories and sub-categories. The set ofpredefined rules may indicate how categories are related to each otherwith in a hierarchy and how product SKU's are related to the categories.In one aspect, category membership may be implicit determined by thetruth of a Boolean expression over one or more attributes of the SKUs ormembership property may be explicitly captured as fact in theontologies. The set of predefined rules may further indicate the set ofscenarios under which the categories and their memberships may changeover period of time. A sub-query pertaining to changes in merchandizehierarchy may be directed to the ontologies about categories and theirevolution whereas a sub query about sales performance of a store may bedirected to another ontology which relates sales data, products andstore concepts to sales performance metrics and the associatedaggregation and analytics methods.

The description of the various ontologies is described in the table 1.

Ontology (Knowledge Base) Description Cognitive Decision-Making Thisontology contains knowledge used to Process Ontology 222 derive theoverall SEAL process. Perceptual System This ontology contains knowledgeabout data Ontology 224 sources, connections, data streams, andknowledge required to create opportunity input data packages. DomainOpportunity This ontology contains the specifications of Ontology 226opportunity types and opportunity instances specific to the domain ofthe business. Domain Knowledge This ontology contains miscellaneousOntology 228 knowledge about the domain of the business. HumanInteraction This ontology contains knowledge about System Ontology 230users, their profiles, and interactions with the knowledge system 102.

After executing the one or more queries, the knowledge processing module214 creates an integrated knowledge object for the knowledge request.The knowledge processing module 214 integrates the knowledge requestupon integrating the one or more knowledge objects fetched by the one ormore sub queries. Subsequent to the creation of the integrated knowledgeobject, the knowledge processing module 214 stores the integratedknowledge object, along with the one or more ontologies, in a temporarymemory (hereinafter referred to as an In-Memory Knowledge Graph) for theuser's reference thereby retrieving the knowledge object pertaining tothe query. In one embodiment, the knowledge processing module 214maintains a cache of knowledge objects in the In-Memory Knowledge Graphin order to speed up processing time of the structured query. Thus, inthis manner, the knowledge system 102 retrieves the knowledge objectrelevant for the business opportunity and thereby allowing the user totake necessary decisions for optimizing the business performance.

In order to elucidate the aforementioned methodology of the knowledgeaccess module 212 and the knowledge processing module 214, consider ascenario where a business opportunity (Excess stock) is being sensed andrequire appropriate attention to manage the Excess stock situation. Totake necessary decisions, a store manager of a warehouse requests a listof Stock Keeping Units having Maximum Retail Price (MRP) greater than‘$50’ and have been kept in the warehouse for more than ‘1’ month. Basedon the request, the knowledge access module 212 generates a structuredquery (SQL query) based on the knowledge request. In one aspect, the SQLquery for the above request is below.

Select SKU_name from [Table(s), Database] where MRP>‘$50’ andduration>‘1’

Please note that the above SQL query is only for representation purpose.The actual SQL query generated may be different then the query asillustrated above. It may be noted that the ‘SKU_name’ indicates “Nameof SKU”, Table, Database indicate a Database storing a plurality ofknowledge objects.

Subsequently, the knowledge access module 212 splits the structuredquery into one or more sub queries, wherein the structured query issplit based on the domain and metadata associated to the domain. Sincethe domain for the above structured query is related to ‘StockManagement’, the knowledge access module 212 splits the structured queryinto two sub queries based on ‘MRP’ and ‘Duration’. Upon splitting thestructured query, the knowledge access module 212 assigns an actorresponsible for fetching at least one knowledge object relevant for eachsub query. Each actor may then refer to the ontologies in order toretrieve the knowledge object relevant to the sub query.

For example, Actor ‘A₁’ refers to the Domain Knowledge Ontology 228 toretrieve the knowledge object ‘KO₁’ pertaining to ‘Finance’ domain.Similarly, Actor ‘A₂’ refers to the Domain Knowledge Ontology 228 toretrieve the knowledge object ‘KO₂’ pertaining to ‘Stock Management’.Both ‘KO₁’ and ‘KO₂’ are then integrated to create an integratedknowledge object (IKO) for the store manager's reference. Thus, in thismanner, the knowledge system 102 retrieves the knowledge object (IKO)relevant for the business opportunity (Excess stock) and therebyallowing the user to take necessary decisions for managing the excessstock situation.

In an exemplary embodiment of the invention, the knowledge system 102further comprises the knowledge persistence module 216 to hide anunderlying storage/database technology. Since the aforementionedmethodology may be implemented in the DCE, it may be understood that theknowledge system may be communicatively coupled with varying databaseshaving distinct protocols. Therefore, it becomes utmost import to hidethe underlying storage/database technology so that any database may beused with the knowledge system 102 for retrieving the knowledge object.By hiding, the knowledge persistence module 216 further facilitates tomigrate the data to an existing database to other database by simplechanging a network path from the existing database to a new database.

In addition to the above, the knowledge persistence module 216 providesa virtualized view of the knowledge object to the user on a GraphicalUser Interface (GUI). The knowledge persistence module 216 furtherprovides a uniform and consistent APIs for all kinds of knowledgeobjects for the knowledge access module 212. In one aspect, theknowledge persistence module 216 further facilitates serialization anddes-serialization capabilities as the knowledge access module 212retrieves the knowledge object upon referring to the one or moreontologies.

Referring now to FIG. 3, a method 300 for retrieving a knowledge object,pertaining to a query, in a cognitive decision-making process is shown,in accordance with an embodiment of the present subject matter. Themethod 300 may be described in the general context of computerexecutable instructions. Generally, computer executable instructions caninclude routines, programs, objects, components, data structures,procedures, modules, functions, etc., that perform particular functionsor implement particular abstract data types. The method 300 may also bepracticed in a distributed computing environment where functions areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, computerexecutable instructions may be located in both local and remote computerstorage media, including memory storage devices.

The order in which the method 300 is described is not intended to beconstrued as a limitation, and any number of the described method blockscan be combined in any order to implement the method 300 or alternatemethods. Additionally, individual blocks may be deleted from the method300 without departing from the spirit and scope of the subject matterdescribed herein. Furthermore, the method can be implemented in anysuitable hardware, software, firmware, or combination thereof. However,for ease of explanation, in the embodiments described below, the method300 may be considered to be implemented as described in the knowledgesystem 102.

At block 302, a knowledge request requesting a knowledge object may bereceived from a user. In one aspect, the knowledge request may beassociated to at least one domain. In one implementation, the knowledgerequest may be received by the knowledge access module 212.

At block 304, a structured query may be generated based on the knowledgerequest. In one implementation, the structured query may be generated bythe knowledge access module 212.

At block 306, the structured query may be split into one or more subqueries. In one aspect, the structured query may be split based on theat least one domain and metadata associated to the at least one domain.In one implementation, the structured query may be split by theknowledge access module 212.

At block 308, one or more knowledge objects may be fetched for each subquery upon executing the one or more sub queries on a system databasestoring a plurality of knowledge objects. In one aspect, the one or moreknowledge objects may be fetched upon referring to one or moreontologies stored in the system database. In one implementation, the oneor more knowledge objects may be fetched by the knowledge access module212.

At block 310, an integrated knowledge object may be created for theknowledge request upon integrating the one or more knowledge objectsfetched by the one or more sub queries. In one implementation, theintegrated knowledge object may be created by the knowledge processingmodule 214.

At block 312, the integrated knowledge object, along with the one ormore ontologies, may be stored in a temporary memory for the user'sreference thereby retrieving the knowledge object pertaining to thequery. In one implementation, the integrated knowledge object, alongwith the one or more ontologies, may be stored by the knowledgeprocessing module 214.

Thus, in this manner, the knowledge system 102 for retrieving theknowledge object in a cognitive decision-making process. Althoughimplementations of a method and system for retrieving the knowledgeobject have been described in language specific to structural featuresand/or methods, it is to be understood that the implementations and/orembodiments are not necessarily limited to the specific features ormethods described.

The invention claimed is:
 1. A method for retrieving a knowledge object,pertaining to a query, in a cognitive decision-making process, themethod comprising: receiving, by a processor, a knowledge requestrequesting a knowledge object, wherein the knowledge request isassociated to at least one domain, and wherein the knowledge request isreceived from a user in a narrative form; generating, by the processor,a structured query based on the knowledge request, wherein thestructured query is generated by using one or more machine learningmethods including a deep learning network pre-trained for the knowledgerequest and a structured query language type; splitting, by theprocessor, the structured query into one or more sub queries, whereinthe structured query is split based on the at least one domain andmetadata associated to the at least one domain; assigning, by theprocessor, an actor for fetching one or more knowledge objects for eachsub query upon executing the one or more sub queries on multipledatabases storing a plurality of knowledge objects, wherein the one ormore knowledge objects are fetched upon referring to one or moreontologies stored in the multiple databases, and wherein the actorindicates a mathematical model for performing computations, and whereinthe one or more sub queries may be executed, in parallel or in adistributed manner, over the multiple databases present in a DistributedComputing Environment (DCE); creating, by the processor, an integratedknowledge object for the knowledge request upon integrating the one ormore knowledge objects fetched by the one or more sub queries; storing,by the processor, the integrated knowledge object, along with the one ormore ontologies, in a temporary memory for the user's reference therebyretrieving the knowledge object pertaining to the query; and generating,by the processor, a virtualized view of the integrated knowledge objectby hiding an underlying storage or a database technology, wherein thevirtualized view of the integrated knowledge object is accessed by theuser through a Graphical User interface.
 2. The method of claim 1,wherein the structured query is generated as one of a Structured QueryLanguage (SQL) and SPARQL.
 3. The method of claim 1, wherein themetadata indicates one or more sub sections associated to the at leastone domain.
 4. The method of claim 1, wherein the one or more ontologiescomprises a set of predefined rules facilitating fetching of a knowledgeobject relevant for a sub query, and wherein the set of predefined rulescomprises relationship among domains and a set of scenarios indicating achange in the relationship among the domains over a period of time. 5.The method of claim 1, wherein each sub query is assigned with an actorresponsible for fetching at least one knowledge object, from the systemdatabase, relevant for a sub query and thereby storing the at least oneknowledge object in the temporary memory.
 6. A knowledge system forretrieving a knowledge object, pertaining to a query, in a cognitivedecision-making process, the knowledge system comprising: a processor;and a memory coupled to the processor, wherein the processor is capableof executing a plurality of modules stored in the memory, and whereinthe plurality of modules comprising a knowledge access module forreceiving a knowledge request requesting a knowledge object, wherein theknowledge request is associated to at least one domain, and wherein theknowledge request is received from a user in a narrative form,generating a structured query based on the knowledge request, whereinthe structured query is generated by using one or more machine learningmethods including a deep learning network pre-trained for the knowledgerequest and a structured query language type, splitting the structuredquery into one or more sub queries, wherein the structured query issplit based on the at least one domain and metadata associated to the atleast one domain, assigning an actor for fetching one or more knowledgeobjects for each sub query upon executing the one or more sub queries onmultiple databases storing a plurality of knowledge objects, wherein theone or more knowledge objects are fetched upon referring to one or moreontologies stored in the multiple databases, and wherein the actorindicates a mathematical model for performing computations, and whereinthe one or more sub queries may be executed, in parallel or in adistributed manner, over the multiple databases present in a DistributedComputing Environment (DCE); and a knowledge processing module forcreating an integrated knowledge object for the knowledge request uponintegrating the one or more knowledge objects fetched by the one or moresub queries, and storing the integrated knowledge object, along with theone or more ontologies, in a temporary memory for the user's referencethereby retrieving the knowledge object pertaining to the query; and aknowledge persistence module for generating a virtualized view of theintegrated knowledge object by hiding an underlying storage or adatabase technology, wherein the virtualized view of the integratedknowledge object is accessed by the user through a Graphical UserInterface.
 7. The knowledge system of claim 6, wherein the structuredquery is generated as one of a Structured Query Language (SQL) andSPARQL.
 8. The knowledge system of claim 6, wherein the metadataindicates one or more sub sections associated to the at least onedomain.
 9. The knowledge system of claim 6, wherein the one or moreontologies comprises a set of predefined rules facilitating fetching ofa knowledge object relevant for a sub query, and wherein the set ofpredefined rules comprises relationship among domains and a set ofscenarios indicating a change in the relationship among the domains overa period of time.
 10. The knowledge system of claim 6, wherein each subquery is assigned with an actor responsible for fetching at least oneknowledge object, from the system database, relevant for a sub query andthereby storing the at least one knowledge object in the temporarymemory.
 11. A non-transitory computer readable medium embodying aprogram executable in a computing device for retrieving a knowledgeobject, pertaining to a query, in a cognitive decision-making process,the program comprising a program code: a program code for receiving aknowledge request requesting a knowledge object, wherein the knowledgerequest is associated to at least one domain, and wherein the knowledgerequest is received from a user in a narrative form; a program code forgenerating a structured query based on the knowledge request, whereinthe structured query is generated by using one or more machine learningmethods including a deep learning network pre-trained for the knowledgerequest and a structured query language type; a program code forsplitting the structured query into one or more sub queries, wherein thestructured query is split based on the at least one domain and metadataassociated to the at least one domain; a program code for assigning anactor for fetching one or more knowledge objects for each sub query uponexecuting the one or more sub queries on multiple databases storing aplurality of knowledge objects, wherein the one or more knowledgeobjects are fetched upon referring to one or more ontologies stored inthe multiple databases, and wherein the actor indicates a mathematicalmodel for performing computations, and wherein the one or more subqueries may be executed, in parallel or in a distributed manner, overthe multiple databases present in a Distributed Computing Environment(DCE); a program code for creating an integrated knowledge object forthe knowledge request upon integrating the one or more knowledge objectsfetched by the one or more sub queries; a program code for storing theintegrated knowledge object, along with the one or more ontologies, in atemporary memory for the user's reference thereby retrieving theknowledge object pertaining to the query; and a program code forgenerating a virtualized view of the integrated knowledge object byhiding an underlying storage or a database technology, wherein thevirtualized view of the integrated knowledge object is accessed by theuser through a Graphical User Interface.